Cyber threats are rapidly evolving, and the number of attacks that organizations face is constantly increasing. With a global shortage of 3.4 million skilled cybersecurity professionals (source: (ISC)2 Workforce Study), organizations must improve the efficiency and effectiveness of their security efforts.
AI and ML have transformed many fields, and incident response is no exception. By leveraging vast amounts of data, these technologies enable organizations to not only detect threats but also respond to security incidents in real-time. Unlike manual approaches, AI and ML-powered automated response systems are faster and less prone to human errors.
Let’s look at how AI-powered solutions are influencing incident response, with a focus on the concept of self-healing endpoints and the challenges that organizations must address when implementing these innovative solutions.
Role of AI and ML in incident response
By analyzing historical security and threat intelligence data points, AI/ML algorithms can identify attack patterns, allowing organizations to proactively implement preventative measures, such as upgrading software, patching vulnerabilities, and updating access control rules.
This enables an organization to stay one step ahead of the attackers.
AI/ML algorithms can help with incident triage and prioritization based on severity, potential impact, and relevance. By analyzing incident attributes, historical data, and contextual data, autonomous response systems can categorize incidents and allocate resources.
This ensures that critical incidents receive immediate attention while optimizing the allocation of security team resources.
AI/ML algorithms can facilitate automated investigations by analyzing large volumes of log data, system events, and network traffic. They can also process threat intelligence data from multiple sources to gain insights into the most recent attack techniques and vulnerabilities.
By identifying correlations and patterns between various alerts and threat intelligence data, AI can help security teams understand attack vectors and take preventative measures to avoid similar incidents in the future.
In the context of cybersecurity, autonomous response focuses on automating various aspects of incident response processes, including containment, remediation, and recovery.
The goal is to mitigate the impact of security incidents by reducing response time, as human intervention might introduce delays that adversaries can exploit.
Once a security incident is detected, autonomous response systems can take quick action based on predefined rules, policies, and machine learning models. These actions can include isolating affected devices or network segments, blocking malicious traffic, quarantining infected files, terminating suspicious processes, applying security patches, disabling compromised user accounts, and initiating countermeasures to neutralize threats. Another key feature of an autonomous response system is its ability to analyze historical data to learn from past incidents and evolve response mechanisms.
When all of the above principles are applied to endpoints, the concept of self-healing endpoints emerges. Traditional incident response often requires significant manual intervention to identify and remediate compromised systems. Self-healing endpoints, on the other hand, use AI and ML algorithms to automatically detect, isolate, and remediate security incidents without human intervention. These endpoints continuously monitor and analyze system behavior, enabling proactive threat detection and autonomous response, resulting in shorter response times and a lower chance of broad compromise.
These endpoints can proactively detect anomalies and potential security threats by continuously monitoring their behavior and network communications. This proactive approach not only reduces the need for constant human intervention, but also helps in risk detection and mitigation, strengthening overall security posture.
Challenges and benefits
In addition to automating response actions like isolating affected endpoints, blocking malicious traffic, and cleaning infected files, self-healing endpoints offer several compelling benefits for organizations seeking to enhance their incident response capabilities, such as:
Self-healing endpoints can detect and respond to security incidents in real-time without the need for human intervention. This enables faster response times, minimizing the impact of security breaches and any subsequent business disruptions.
With self-healing capabilities, routine tasks such as system updates, patch management, and malware scanning can be automated. This can significantly reduce the burden on IT and security teams, allowing them to focus on more complex and strategic tasks.
Consistency and scalability
As the number of endpoints increase, self-healing capabilities ensure that each device is protected and responds to incidents in a consistent manner, offering comprehensive security coverage. This allows for security measures to be implemented consistently across the entire network, irrespective of its size.
Having said that, the implementation of self-healing endpoints presents several challenges that organizations must address. Apart from the generic challenges associated with AI-powered systems, such as integration complexity and context analysis, organizations must consider the following:
- False positives and negatives
The accuracy of AI and ML algorithms is critical for self-healing endpoints. False positives, in which legitimate activities are flagged as anomalies, might lead to unnecessary actions or disruptions. False negatives, on the other hand, which might occur when legitimate security incidents go undetected, will allow threats to remain undetected.
Organizations must invest in robust and continuous training of their AI models to ensure accuracy and adaptability to evolving threats. Additionally, transparency and clarity of AI-driven decisions are essential for establishing trust and compliance with regulatory frameworks.
- Responsibility and accountability
When AI performs an action autonomously, it is difficult to assign responsibility in case of mistakes. Establishing accountability guidelines and clearly defining areas where human oversight is needed for decision-making can help in striking the right balance for human-AI collaboration.
- Resilience and robustness
An attacker may target the logic that governs self-healing capabilities, causing the system to perform an unwanted action or go dormant. For example, an attacker could deliberately launch attacks that trigger the response mechanism to isolate multiple endpoints at the same time, leading to a denial of service attack.
Implementing AI-powered response capabilities
While AI-powered response has several benefits, organizations should assess their maturity level, budget and pain points before implementing its capabilities. It is crucial to evaluate existing incident response capabilities and identify areas where automation can add the most value.
Additionally, only the biggest and best-equipped organizations can afford to build an infrastructure which can ingest large volumes of data and continuously train their AI models. Organizations with limited resources may choose to start with smaller-scale implementations and gradually expand as they gain experience and confidence. Alternately, they can push their cybersecurity providers to do for them.
Moreover, organizations should invest in comprehensive training for their security teams to ensure a smooth transition to automated response systems. Building a strong foundation in AI/ML concepts will enable the workforce to effectively leverage these technologies and understand their limitations.
Organizations must also establish robust governance frameworks to address ethical concerns and ensure compliance with privacy regulations.
AI and ML technologies have immense potential for revolutionizing incident response through automated systems. Self-healing endpoints represent a paradigm shift in how organizations detect and respond to cyber threats. By embracing these innovations, organizations can augment their incident response capabilities, reduce response times, and mitigate the impact of security incidents. However, careful consideration of accuracy, transparency, and training is vital to overcoming the challenges associated with the adoption of AI and ML.
As organizations continue to explore these new use cases, they must strike a balance between automation and human expertise to ensure a robust and resilient security posture in the face of ever evolving cyber threats.
About the author
Global Head of Data Science, Managed Detection and Response (MDR)
Harshvardhan Parmar is Global Head of Data Science, Managed Detection and Response (MDR)
Harshvardhan currently heads the Data Science division for Managed Detection and Response (MDR) at Eviden. His work involves establishing the vision, mission of using data science to detect advanced cybersecurity threats and overseeing the creation of various Artificial Intelligence (AI) models and algorithms used in AIsaac – Eviden’s next-gen AI platform used for delivering MDR service.
Harshvardhan has been working in cybersecurity for 13 years, during which he has directly serviced large enterprises and Fortune 500 customers across US, Europe, and Asia Pacific. He currently holds 2 U.S. patents in AI & Cybersecurity. He is also a Certified Information Systems Auditor